Conference item
Proportional justified representation
- Abstract:
- The goal of multi-winner elections is to choose a fixed-size committee based on voters’ preferences. An important concern in this setting is representation: large groups of voters with cohesive preferences should be adequately represented by the election winners. Recently, Aziz et al. (2015a; 2017) proposed two axioms that aim to capture this idea: justified representation (JR) and its strengthening extended justified representation (EJR). In this paper, we extend the work of Aziz et al. in several directions. First, we answer an open question of Aziz et al., by showing that Reweighted Approval Voting satisfies JR for k = 3; 4; 5, but fails it for k ≥ 6. Second, we observe that EJR is incompatible with the Perfect Representation criterion, which is important for many applications of multi-winner voting, and propose a relaxation of EJR, which we call Proportional Justified Representation (PJR). PJR is more demanding than JR, but, unlike EJR, it is compatible with perfect representation, and a committee that provides PJR can be computed in polynomial time if the committee size divides the number of voters. Moreover, just like EJR, PJR can be used to characterize the classic PAV rule in the class of weighted PAV rules. On the other hand, we show that EJR provides stronger guarantees with respect to average voter satisfaction than PJR does.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Accepted manuscript, pdf, 233.1KB, Terms of use)
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Authors
+ Spanish Ministry of Economy and Competitiveness
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- Grant:
- HERMES-SMARTDRIVER TIN2013-46801-C4-2-R
- Publisher:
- AAAI Press
- Host title:
- Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17)
- Journal:
- AAAI Conference on Artificial Intelligence 2017 More from this journal
- Pages:
- 670-676
- Publication date:
- 2017-02-01
- Acceptance date:
- 2016-11-12
- ISSN:
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2159-5399
- Keywords:
- Pubs id:
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pubs:665378
- UUID:
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uuid:f159a981-0b99-483e-a408-2ec11d4abbba
- Local pid:
-
pubs:665378
- Source identifiers:
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665378
- Deposit date:
-
2016-12-13
- ARK identifier:
Terms of use
- Copyright holder:
- Association for the Advancement of Artificial Intelligence
- Copyright date:
- 2017
- Notes:
-
Copyright © 2017, Association for the Advancement of Artificial
Intelligence This is the accepted manuscript version of the article. The final version is available online from AAAI Press at: http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14917
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